A review on adhesion behavior of chip seal pavement and aggregate
Bibliographic record
Abstract
Chip seal is widely used for preventive maintenance to mitigate pavement deterioration, but it is prone to aggregate loss during pavement service. To further promote the development and application of chip seals in road engineering in China, the research progress of the adhesion behavior of aggregate and binder in chip seals was reviewed in this paper, focusing on the adhesion mechanism of emulsified asphalt and alkaline aggregate. The Influencing factors and evaluation methodology of chip seals' aggregate adhesion behavior were also discussed. The results demonstrate that the adhesion process between emulsified asphalt and alkaline aggregate is divided into three processes including infiltration, demulsification, and cluster, which is more complicated when compared to hot asphalt. When designing a chip seal, not only the characteristics of single material should be paid attention to, but also the combination of binder and aggregate matters a lot. To form good adhesion between aggregate and asphalt binder, various influencing factors such as material selection, design method, and construction technical index should be considered comprehensively in the whole design, construction, and operation process. Three methods for evaluating adhesion behavior are summarized, including macroscopic adhesion performance tests, image analysis technology, and model prediction. It is not objective to evaluate the aggregate adhesion behavior of chip seal only by a single evaluation method. A comprehensive evaluation based on the micro-macro multi-scale method should be considered in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".